Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations324
Missing cells68
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.8 KiB
Average record size in memory258.4 B

Variable types

Categorical1
Numeric10
Text2

Alerts

arma de fogo apreendida is highly overall correlated with estupro and 6 other fieldsHigh correlation
estupro is highly overall correlated with arma de fogo apreendida and 5 other fieldsHigh correlation
estupro de vulnerável is highly overall correlated with ufHigh correlation
furto de veiculo is highly overall correlated with arma de fogo apreendida and 6 other fieldsHigh correlation
morte por intervencao de agente do estado is highly overall correlated with arma de fogo apreendida and 4 other fieldsHigh correlation
pessoa desaparecida is highly overall correlated with arma de fogo apreendida and 6 other fieldsHigh correlation
pessoa localizada is highly overall correlated with arma de fogo apreendida and 6 other fieldsHigh correlation
tráfico de drogas is highly overall correlated with arma de fogo apreendida and 6 other fieldsHigh correlation
uf is highly overall correlated with arma de fogo apreendida and 6 other fieldsHigh correlation
apreensao de cocaina has 34 (10.5%) missing values Missing
apreensao de maconha has 34 (10.5%) missing values Missing
uf is uniformly distributed Uniform
estupro de vulnerável has 179 (55.2%) zeros Zeros
morte por intervencao de agente do estado has 29 (9.0%) zeros Zeros
morte de agente do estado has 230 (71.0%) zeros Zeros

Reproduction

Analysis started2025-05-22 14:37:34.449250
Analysis finished2025-05-22 14:37:43.538731
Duration9.09 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

uf
Categorical

High correlation  Uniform 

Distinct27
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
AC
 
12
AL
 
12
AM
 
12
AP
 
12
BA
 
12
Other values (22)
264 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters648
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAC
3rd rowAC
4th rowAC
5th rowAC

Common Values

ValueCountFrequency (%)
AC 12
 
3.7%
AL 12
 
3.7%
AM 12
 
3.7%
AP 12
 
3.7%
BA 12
 
3.7%
CE 12
 
3.7%
DF 12
 
3.7%
ES 12
 
3.7%
GO 12
 
3.7%
MA 12
 
3.7%
Other values (17) 204
63.0%

Length

2025-05-22T11:37:43.667217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 12
 
3.7%
al 12
 
3.7%
am 12
 
3.7%
ap 12
 
3.7%
ba 12
 
3.7%
ce 12
 
3.7%
df 12
 
3.7%
es 12
 
3.7%
go 12
 
3.7%
ma 12
 
3.7%
Other values (17) 204
63.0%

Most occurring characters

ValueCountFrequency (%)
A 84
13.0%
P 84
13.0%
R 84
13.0%
S 72
11.1%
M 60
9.3%
E 48
7.4%
O 36
 
5.6%
C 36
 
5.6%
B 24
 
3.7%
T 24
 
3.7%
Other values (7) 96
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 84
13.0%
P 84
13.0%
R 84
13.0%
S 72
11.1%
M 60
9.3%
E 48
7.4%
O 36
 
5.6%
C 36
 
5.6%
B 24
 
3.7%
T 24
 
3.7%
Other values (7) 96
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 84
13.0%
P 84
13.0%
R 84
13.0%
S 72
11.1%
M 60
9.3%
E 48
7.4%
O 36
 
5.6%
C 36
 
5.6%
B 24
 
3.7%
T 24
 
3.7%
Other values (7) 96
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 84
13.0%
P 84
13.0%
R 84
13.0%
S 72
11.1%
M 60
9.3%
E 48
7.4%
O 36
 
5.6%
C 36
 
5.6%
B 24
 
3.7%
T 24
 
3.7%
Other values (7) 96
14.8%

data_referencia
Real number (ℝ)

Distinct12
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:43.756534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4573921
Coefficient of variation (CV)0.53190648
Kurtosis-1.2170219
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum2106
Variance11.95356
MonotonicityNot monotonic
2025-05-22T11:37:43.807890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 27
8.3%
2 27
8.3%
3 27
8.3%
4 27
8.3%
5 27
8.3%
6 27
8.3%
7 27
8.3%
8 27
8.3%
9 27
8.3%
10 27
8.3%
Other values (2) 54
16.7%
ValueCountFrequency (%)
1 27
8.3%
2 27
8.3%
3 27
8.3%
4 27
8.3%
5 27
8.3%
6 27
8.3%
7 27
8.3%
8 27
8.3%
9 27
8.3%
10 27
8.3%
ValueCountFrequency (%)
12 27
8.3%
11 27
8.3%
10 27
8.3%
9 27
8.3%
8 27
8.3%
7 27
8.3%
6 27
8.3%
5 27
8.3%
4 27
8.3%
3 27
8.3%

apreensao de cocaina
Text

Missing 

Distinct282
Distinct (%)97.2%
Missing34
Missing (%)10.5%
Memory size18.9 KiB
2025-05-22T11:37:44.037825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.3896552
Min length1

Characters and Unicode

Total characters1563
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)94.5%

Sample

1st row10,154
2nd row19,155
3rd row25,542
4th row33,751
5th row92,494
ValueCountFrequency (%)
39 2
 
0.7%
32 2
 
0.7%
7 2
 
0.7%
16 2
 
0.7%
9 2
 
0.7%
19 2
 
0.7%
0 2
 
0.7%
2 2
 
0.7%
57,53 1
 
0.3%
9,3 1
 
0.3%
Other values (272) 272
93.8%
2025-05-22T11:37:44.326165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 249
15.9%
1 205
13.1%
5 156
10.0%
2 153
9.8%
7 131
8.4%
4 128
8.2%
3 125
8.0%
6 115
7.4%
9 106
6.8%
8 105
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 249
15.9%
1 205
13.1%
5 156
10.0%
2 153
9.8%
7 131
8.4%
4 128
8.2%
3 125
8.0%
6 115
7.4%
9 106
6.8%
8 105
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 249
15.9%
1 205
13.1%
5 156
10.0%
2 153
9.8%
7 131
8.4%
4 128
8.2%
3 125
8.0%
6 115
7.4%
9 106
6.8%
8 105
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 249
15.9%
1 205
13.1%
5 156
10.0%
2 153
9.8%
7 131
8.4%
4 128
8.2%
3 125
8.0%
6 115
7.4%
9 106
6.8%
8 105
6.7%

apreensao de maconha
Text

Missing 

Distinct287
Distinct (%)99.0%
Missing34
Missing (%)10.5%
Memory size19.0 KiB
2025-05-22T11:37:44.519389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.0655172
Min length1

Characters and Unicode

Total characters1759
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique284 ?
Unique (%)97.9%

Sample

1st row14,998
2nd row48,802
3rd row65,244
4th row610,435
5th row334,598
ValueCountFrequency (%)
11 2
 
0.7%
0,342 2
 
0.7%
5 2
 
0.7%
1731,061 1
 
0.3%
2206,9 1
 
0.3%
299,66 1
 
0.3%
1883,46 1
 
0.3%
2038,08 1
 
0.3%
9106,32 1
 
0.3%
3572,21 1
 
0.3%
Other values (277) 277
95.5%
2025-05-22T11:37:44.785741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 242
13.8%
1 233
13.2%
2 166
9.4%
4 161
9.2%
3 151
8.6%
8 142
8.1%
9 137
7.8%
6 137
7.8%
5 134
7.6%
7 129
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 242
13.8%
1 233
13.2%
2 166
9.4%
4 161
9.2%
3 151
8.6%
8 142
8.1%
9 137
7.8%
6 137
7.8%
5 134
7.6%
7 129
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 242
13.8%
1 233
13.2%
2 166
9.4%
4 161
9.2%
3 151
8.6%
8 142
8.1%
9 137
7.8%
6 137
7.8%
5 134
7.6%
7 129
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 242
13.8%
1 233
13.2%
2 166
9.4%
4 161
9.2%
3 151
8.6%
8 142
8.1%
9 137
7.8%
6 137
7.8%
5 134
7.6%
7 129
7.3%

estupro
Real number (ℝ)

High correlation 

Distinct187
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.69444
Minimum0
Maximum683
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:44.851088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.15
Q148
median80.5
Q3188.5
95-th percentile505.8
Maximum683
Range683
Interquartile range (IQR)140.5

Descriptive statistics

Standard deviation154.20818
Coefficient of variation (CV)1.037081
Kurtosis1.3966462
Mean148.69444
Median Absolute Deviation (MAD)55.5
Skewness1.5297832
Sum48177
Variance23780.163
MonotonicityNot monotonic
2025-05-22T11:37:44.943133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 5
 
1.5%
53 5
 
1.5%
61 5
 
1.5%
82 4
 
1.2%
65 4
 
1.2%
93 4
 
1.2%
155 4
 
1.2%
52 4
 
1.2%
56 4
 
1.2%
21 4
 
1.2%
Other values (177) 281
86.7%
ValueCountFrequency (%)
0 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
10 2
0.6%
11 2
0.6%
12 1
 
0.3%
13 3
0.9%
14 3
0.9%
16 3
0.9%
17 3
0.9%
ValueCountFrequency (%)
683 1
0.3%
641 1
0.3%
623 1
0.3%
621 1
0.3%
597 1
0.3%
580 1
0.3%
572 1
0.3%
565 1
0.3%
554 1
0.3%
541 1
0.3%

estupro de vulnerável
Real number (ℝ)

High correlation  Zeros 

Distinct126
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.16049
Minimum0
Maximum1219
Zeros179
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.024457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3118.25
95-th percentile413.4
Maximum1219
Range1219
Interquartile range (IQR)118.25

Descriptive statistics

Standard deviation215.90479
Coefficient of variation (CV)1.9422799
Kurtosis10.868899
Mean111.16049
Median Absolute Deviation (MAD)0
Skewness3.1434063
Sum36016
Variance46614.878
MonotonicityNot monotonic
2025-05-22T11:37:45.121045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 179
55.2%
55 4
 
1.2%
108 3
 
0.9%
114 3
 
0.9%
91 3
 
0.9%
64 2
 
0.6%
87 2
 
0.6%
135 2
 
0.6%
263 2
 
0.6%
66 2
 
0.6%
Other values (116) 122
37.7%
ValueCountFrequency (%)
0 179
55.2%
30 1
 
0.3%
33 1
 
0.3%
36 1
 
0.3%
39 1
 
0.3%
42 1
 
0.3%
45 1
 
0.3%
48 1
 
0.3%
52 1
 
0.3%
53 1
 
0.3%
ValueCountFrequency (%)
1219 1
0.3%
1123 1
0.3%
1110 1
0.3%
1105 1
0.3%
1080 1
0.3%
1047 1
0.3%
1023 1
0.3%
1013 1
0.3%
994 1
0.3%
980 1
0.3%

furto de veiculo
Real number (ℝ)

High correlation 

Distinct252
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664.13889
Minimum15
Maximum8812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.195546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile44
Q1134.75
median238.5
Q3542.25
95-th percentile2104.55
Maximum8812
Range8797
Interquartile range (IQR)407.5

Descriptive statistics

Standard deviation1478.2076
Coefficient of variation (CV)2.2257508
Kurtosis18.354124
Mean664.13889
Median Absolute Deviation (MAD)141
Skewness4.3125826
Sum215181
Variance2185097.8
MonotonicityNot monotonic
2025-05-22T11:37:45.284466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 3
 
0.9%
152 3
 
0.9%
160 3
 
0.9%
148 3
 
0.9%
180 3
 
0.9%
165 3
 
0.9%
157 3
 
0.9%
248 3
 
0.9%
207 3
 
0.9%
224 3
 
0.9%
Other values (242) 294
90.7%
ValueCountFrequency (%)
15 1
0.3%
18 1
0.3%
22 1
0.3%
24 1
0.3%
29 1
0.3%
30 1
0.3%
34 1
0.3%
37 2
0.6%
39 1
0.3%
40 2
0.6%
ValueCountFrequency (%)
8812 1
0.3%
8254 1
0.3%
8118 1
0.3%
7882 1
0.3%
7872 1
0.3%
7830 1
0.3%
7699 1
0.3%
7688 1
0.3%
7615 1
0.3%
7522 1
0.3%

morte por intervencao de agente do estado
Real number (ℝ)

High correlation  Zeros 

Distinct73
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.947531
Minimum0
Maximum166
Zeros29
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.383913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q318.25
95-th percentile77.4
Maximum166
Range166
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation28.966945
Coefficient of variation (CV)1.5287979
Kurtosis8.093283
Mean18.947531
Median Absolute Deviation (MAD)6
Skewness2.7158844
Sum6139
Variance839.08393
MonotonicityNot monotonic
2025-05-22T11:37:45.461725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
9.0%
1 25
 
7.7%
5 23
 
7.1%
4 21
 
6.5%
3 16
 
4.9%
6 16
 
4.9%
2 16
 
4.9%
7 14
 
4.3%
9 12
 
3.7%
14 11
 
3.4%
Other values (63) 141
43.5%
ValueCountFrequency (%)
0 29
9.0%
1 25
7.7%
2 16
4.9%
3 16
4.9%
4 21
6.5%
5 23
7.1%
6 16
4.9%
7 14
4.3%
8 9
 
2.8%
9 12
3.7%
ValueCountFrequency (%)
166 1
0.3%
158 1
0.3%
155 1
0.3%
151 1
0.3%
145 1
0.3%
120 1
0.3%
118 2
0.6%
114 1
0.3%
112 1
0.3%
102 1
0.3%

tráfico de drogas
Real number (ℝ)

High correlation 

Distinct251
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean564.8179
Minimum19
Maximum3436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.539357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile64.3
Q1118
median319.5
Q3526
95-th percentile2182.85
Maximum3436
Range3417
Interquartile range (IQR)408

Descriptive statistics

Standard deviation725.13867
Coefficient of variation (CV)1.2838451
Kurtosis4.6522828
Mean564.8179
Median Absolute Deviation (MAD)201.5
Skewness2.2291203
Sum183001
Variance525826.09
MonotonicityNot monotonic
2025-05-22T11:37:45.621662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 5
 
1.5%
80 4
 
1.2%
93 4
 
1.2%
92 4
 
1.2%
98 3
 
0.9%
186 3
 
0.9%
79 3
 
0.9%
85 3
 
0.9%
431 3
 
0.9%
454 3
 
0.9%
Other values (241) 289
89.2%
ValueCountFrequency (%)
19 1
0.3%
22 1
0.3%
24 2
0.6%
29 1
0.3%
32 1
0.3%
35 1
0.3%
39 1
0.3%
43 2
0.6%
44 1
0.3%
46 1
0.3%
ValueCountFrequency (%)
3436 1
0.3%
3317 1
0.3%
3288 1
0.3%
3219 1
0.3%
3209 1
0.3%
3185 1
0.3%
3181 1
0.3%
3162 1
0.3%
3114 1
0.3%
3049 1
0.3%

arma de fogo apreendida
Real number (ℝ)

High correlation 

Distinct251
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.08333
Minimum16
Maximum1351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.714075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile39.15
Q1117
median185.5
Q3467.5
95-th percentile1044.65
Maximum1351
Range1335
Interquartile range (IQR)350.5

Descriptive statistics

Standard deviation297.52679
Coefficient of variation (CV)0.93832363
Kurtosis2.2479476
Mean317.08333
Median Absolute Deviation (MAD)128
Skewness1.6084576
Sum102735
Variance88522.188
MonotonicityNot monotonic
2025-05-22T11:37:45.803954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 5
 
1.5%
45 4
 
1.2%
145 4
 
1.2%
183 4
 
1.2%
25 4
 
1.2%
40 3
 
0.9%
117 3
 
0.9%
118 3
 
0.9%
35 3
 
0.9%
111 3
 
0.9%
Other values (241) 288
88.9%
ValueCountFrequency (%)
16 1
 
0.3%
22 1
 
0.3%
25 4
1.2%
28 1
 
0.3%
29 1
 
0.3%
31 2
0.6%
32 1
 
0.3%
35 3
0.9%
36 1
 
0.3%
37 1
 
0.3%
ValueCountFrequency (%)
1351 1
0.3%
1285 1
0.3%
1280 1
0.3%
1278 1
0.3%
1267 1
0.3%
1262 1
0.3%
1260 1
0.3%
1252 1
0.3%
1225 1
0.3%
1224 1
0.3%

morte de agente do estado
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63580247
Minimum0
Maximum10
Zeros230
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:45.880464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4262301
Coefficient of variation (CV)2.2431968
Kurtosis13.573477
Mean0.63580247
Median Absolute Deviation (MAD)0
Skewness3.3668697
Sum206
Variance2.0341322
MonotonicityNot monotonic
2025-05-22T11:37:45.939606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 230
71.0%
1 54
 
16.7%
2 12
 
3.7%
3 10
 
3.1%
4 9
 
2.8%
7 4
 
1.2%
5 3
 
0.9%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
0 230
71.0%
1 54
 
16.7%
2 12
 
3.7%
3 10
 
3.1%
4 9
 
2.8%
5 3
 
0.9%
7 4
 
1.2%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
10 1
 
0.3%
9 1
 
0.3%
7 4
 
1.2%
5 3
 
0.9%
4 9
 
2.8%
3 10
 
3.1%
2 12
 
3.7%
1 54
 
16.7%
0 230
71.0%

pessoa desaparecida
Real number (ℝ)

High correlation 

Distinct204
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.94136
Minimum0
Maximum1854
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:46.014156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q155
median96
Q3320.5
95-th percentile693.3
Maximum1854
Range1854
Interquartile range (IQR)265.5

Descriptive statistics

Standard deviation332.61405
Coefficient of variation (CV)1.3415029
Kurtosis10.135325
Mean247.94136
Median Absolute Deviation (MAD)69.5
Skewness2.9889353
Sum80333
Variance110632.1
MonotonicityNot monotonic
2025-05-22T11:37:46.102435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 6
 
1.9%
52 6
 
1.9%
53 5
 
1.5%
28 5
 
1.5%
58 5
 
1.5%
54 4
 
1.2%
59 4
 
1.2%
30 4
 
1.2%
193 4
 
1.2%
63 4
 
1.2%
Other values (194) 277
85.5%
ValueCountFrequency (%)
0 1
 
0.3%
6 1
 
0.3%
13 1
 
0.3%
15 1
 
0.3%
21 1
 
0.3%
23 1
 
0.3%
24 1
 
0.3%
25 1
 
0.3%
27 1
 
0.3%
28 5
1.5%
ValueCountFrequency (%)
1854 1
0.3%
1822 1
0.3%
1772 1
0.3%
1735 1
0.3%
1716 1
0.3%
1667 1
0.3%
1656 1
0.3%
1640 1
0.3%
1571 1
0.3%
1544 1
0.3%

pessoa localizada
Real number (ℝ)

High correlation 

Distinct188
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.71914
Minimum0
Maximum1441
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2025-05-22T11:37:46.201437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.15
Q125
median65
Q3178.5
95-th percentile684.55
Maximum1441
Range1441
Interquartile range (IQR)153.5

Descriptive statistics

Standard deviation276.00737
Coefficient of variation (CV)1.6358984
Kurtosis9.7006457
Mean168.71914
Median Absolute Deviation (MAD)48
Skewness3.0316623
Sum54665
Variance76180.066
MonotonicityNot monotonic
2025-05-22T11:37:46.290632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 8
 
2.5%
19 7
 
2.2%
6 6
 
1.9%
9 5
 
1.5%
12 5
 
1.5%
15 5
 
1.5%
20 5
 
1.5%
18 5
 
1.5%
41 4
 
1.2%
26 4
 
1.2%
Other values (178) 270
83.3%
ValueCountFrequency (%)
0 2
 
0.6%
1 2
 
0.6%
2 1
 
0.3%
3 3
 
0.9%
4 1
 
0.3%
5 8
2.5%
6 6
1.9%
7 1
 
0.3%
9 5
1.5%
10 3
 
0.9%
ValueCountFrequency (%)
1441 1
0.3%
1420 1
0.3%
1415 1
0.3%
1390 1
0.3%
1375 1
0.3%
1361 1
0.3%
1333 1
0.3%
1321 1
0.3%
1262 1
0.3%
1242 1
0.3%

Interactions

2025-05-22T11:37:42.536150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:34.826170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.353538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.135697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.852582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.477317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.281934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.963907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.678366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.336897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.591149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:34.890004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.434704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.205274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.918227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.545333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.351576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.038655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.736478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.401636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.667762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:34.963305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.521193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.280650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.979331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.628196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.412496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.114122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.795167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.989381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.741750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.020185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.589826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.355794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.038823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.704967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.485489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.178361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.875155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.057752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.807190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.086126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.673364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.420304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.084898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.781976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.556676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.237101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.947159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.122421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.874013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.155238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.746550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.497110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.153052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.855152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.629945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.317817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.007487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.188284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.954001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.224237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.836574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.584724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.218783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.961933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.694485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.390035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.078443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.254300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:43.030929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.293561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.914723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.656679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.285404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.064031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.766189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.470118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.152119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.332313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:43.090883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.356515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:36.988131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.721427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.348069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.137191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.830247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.536671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.203651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.403348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:43.171392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:35.431436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.068735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:37.793084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:38.414737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.215460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:39.900970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:40.608834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:41.271955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T11:37:42.468960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-22T11:37:46.368132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
arma de fogo apreendidadata_referenciaestuproestupro de vulnerávelfurto de veiculomorte de agente do estadomorte por intervencao de agente do estadopessoa desaparecidapessoa localizadatráfico de drogasuf
arma de fogo apreendida1.000-0.0310.7210.1140.8880.4170.5910.8520.7080.9110.626
data_referencia-0.0311.0000.0210.0070.004-0.064-0.0320.031-0.0200.0050.000
estupro0.7210.0211.000-0.3560.7540.3390.4890.6600.6010.7540.641
estupro de vulnerável0.1140.007-0.3561.0000.0850.1030.1790.1870.2050.1690.585
furto de veiculo0.8880.0040.7540.0851.0000.4170.5290.8590.7610.9160.782
morte de agente do estado0.417-0.0640.3390.1030.4171.0000.4480.3370.3070.4270.293
morte por intervencao de agente do estado0.591-0.0320.4890.1790.5290.4481.0000.5270.5140.6400.437
pessoa desaparecida0.8520.0310.6600.1870.8590.3370.5271.0000.7800.8640.653
pessoa localizada0.708-0.0200.6010.2050.7610.3070.5140.7801.0000.8450.756
tráfico de drogas0.9110.0050.7540.1690.9160.4270.6400.8640.8451.0000.667
uf0.6260.0000.6410.5850.7820.2930.4370.6530.7560.6671.000

Missing values

2025-05-22T11:37:43.274619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-22T11:37:43.385552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-22T11:37:43.487669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ufdata_referenciaapreensao de cocainaapreensao de maconhaestuproestupro de vulnerávelfurto de veiculomorte por intervencao de agente do estadotráfico de drogasarma de fogo apreendidamorte de agente do estadopessoa desaparecidapessoa localizada
0AC110,15414,99811.055.049.01.073.064.00.030.029.0
1AC219,15548,80219.036.047.00.076.045.00.028.027.0
2AC325,54265,24410.033.044.00.079.050.00.021.018.0
3AC433,751610,43513.045.052.01.081.044.00.030.025.0
4AC592,494334,59827.061.050.00.074.040.00.028.025.0
5AC620,5555,85220.055.063.01.074.046.00.036.034.0
6AC721,219310,59730.030.053.00.071.048.00.032.024.0
7AC829,70250,76714.039.044.01.060.058.00.038.034.0
8AC931,36893,69814.056.050.00.078.036.00.040.031.0
9AC101,73628,92121.055.045.04.059.045.00.039.036.0
ufdata_referenciaapreensao de cocainaapreensao de maconhaestuproestupro de vulnerávelfurto de veiculomorte por intervencao de agente do estadotráfico de drogasarma de fogo apreendidamorte de agente do estadopessoa desaparecidapessoa localizada
314TO3NaNNaN13.066.0116.01.073.079.00.038.023.0
315TO411,61317.076.0105.07.082.0132.01.036.019.0
316TO518,515,611.088.0109.07.091.092.00.044.025.0
317TO68,1324,6313.086.0123.013.066.073.00.038.012.0
318TO7106,7158,888.071.096.04.092.076.00.054.010.0
319TO813,9122821.072.094.01.058.060.00.053.036.0
320TO929,611312.082.0103.03.093.060.02.043.019.0
321TO107,44428,7322.088.0134.05.069.075.01.042.013.0
322TO1110,6929,0875.070.0110.02.078.051.00.058.022.0
323TO127,527,9214.055.0123.01.070.090.00.062.010.0